In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 1.5 billion users subscribing to cellular telephone services and the number is continually increasing. The number of GSM users around the world alone has already crossed the 1.2 billion mark, as an example of the increased use of cellular services. One in five people around the world now has a mobile phone and in some developed markets mobile penetration has already approached 100%. It is predicted that by 2010 there will be over 2.3 billion individual wireless subscribers worldwide.
In some countries, the number of cellular subscribers already exceeds the number of fixed line telephone installations. In many cases, the revenues from mobile services exceeds that for fixed line services even though the amount of traffic generated through mobile phones is less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs), wireless local loops (WLLs), WiMAX and Ultra Wideband (UWB) based MANs.
Currently, the majority of users subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically third generation digital technology. Currently, fourth generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM/GPRS/EGPRS, TDMA (IS-136), CDMA (IS-95), UMTS (WCDMA), etc. Future fourth generation cellular services are intended to provide mobile data at rates of 100 Mbps or more.
A problem exists in wireless communication systems such as GSM, however, where stray signals, or signals intentionally introduced by frequency reuse methods, can interfere with the proper transmission and reception of voice and data signals and can lower capacity. The constant increase in the deployment of cellular networks increases both the levels of background interference and interference due to co-channel transmission. For typical cell layouts, the major source of noise and interference experienced by GSM communication devices when the network is supporting a non-trivial number of users is due to co-channel and/or adjacent channel interference. Such noise sources arise from nearby devices transmitting on or near the same channel as the desired signal, or from adjacent channel interference, such as noise arising on the desired channel due to spectral leakage. A diagram illustrating an example cellular network including a plurality of EDGE transmitters and receivers and GMSK transmitters generating co-channel interference is shown in FIG. 1. The example cellular network, generally referenced 100, comprises an EDGE transmitter 102 and receiver 106 for sending and receiving a main, desired signal. A plurality of GSM transmitters 104, outputting a GMSK signal, generate co-channel interference at the EDGE receiver 106.
The interference from these noise sources is sensed in both mobile terminals and base stations. In areas with dense cellular utilization a severe degradation in network performance is reported due to this effect. Furthermore, cellular operators with low network bandwidth are forced to lower the reuse factor in their networks which further increases the rate of channel co-transmissions. The problem of co-channel transmissions poses a disjoint problem for both the receiver at the base station and the receiver at the mobile station.
For the base station the co-channel interference problem is considered simpler than in the case of mobile terminals. One reason for that is that the higher cost of base station equipment permits the insertion of complex receivers to combat the sensed interferences. The receivers in the base station (1) incorporate algorithms with higher levels of complexity, (2) can have higher power consumption, etc. Another reason the co-channel interference problem is considered simpler in the base station than in the case of mobile terminals is that the base station can utilize smart antennas to help deal with the problem of co-channel interference. Although smart antennas will affect the cost of the base station, its main impact is in the physical size of the antenna. Due to the size of the smart antenna, its use with mobile, portable cellular equipment is severely limited. Its use with base stations, however, is not limited considering the relatively large sized antennas permitted for base stations. The size of base station antennas is practically unbounded and therefore the usage of smart phased array antennas is possible. This enables the use of receive diversity techniques with multi user separation capability.
In the mobile terminal, on the other hand, both complexity and size are crucial factors in the applicability of a solution. Firstly, complex solutions lead to high MIPS consumption which typically causes increased power consumption as well as increased costs. Secondly, the physical size of the mobile terminal is limited. The tiny size of pocket-sized mobile terminals today substantially limits the expected effectiveness in choosing a smart antenna solution, leaving them for base station applications only.
Therefore, in order for cellular networks to remain effective, there is renewed interest in simple interference reduction solutions that are applicable with a single antenna input. Recently, there is great interest in developing an effective interference reduction solution with regard to GSM networks especially for voice applications. This is because the coverage of EGPRS services is expected to increase greatly and it is expected that existing GSM transmissions will appear as co-channel interference to EDGE receivers.
Prior art solutions to the problem can generally be divided into two families: (1) joint solutions where some kind of knowledge about the interference is assumed such as a known training sequence (TSC) and (2) blind solutions where no a priori knowledge regarding the interfering signal is undertaken. The first family, which is considered to result in a more complex solution, may be separated into two subsets: (a) iterative solutions and (b) joint solutions. Iterative solutions correspond to a receiver where the strongest signal is first demodulated and then in a second step, a residual error signal from the first step is demodulated to estimate the co-existing signal (which may be the main signal of interest and the interfering signal as well). This iteration may be performed many times until a stopping criterion is fulfilled. In joint solutions the main signal and the interfering signal are jointly demodulated assuming a joint received signal model.
A blind solution, on the other hand, does not assume any a priori knowledge about the interfering signal. The most common approach taken is to use a higher order statistics model for the interfering signal. Although, a blind solution is expected to be advantageous in terms of complexity and robustness, it is usually at the expense of performance.
Many prior art interference cancellation techniques have focused on adjacent channel suppression which uses several filtering operations to suppress the frequencies of the received signal that are not also occupied by the desired signal. Co-channel interference techniques, such as joint demodulation, generally require joint channel estimation methods to provide a joint determination of the desired and co-channel interfering signal channel impulse responses. Given known training sequences, all the co-channel interferers can be estimated jointly. This joint demodulation technique, however, consumes a large number of MIPS processing, which limits the number of equalization parameters that can be used efficiently. Moreover, classical joint demodulation only addresses one co-channel interferer, and does not address adjacent channel interference.
Multiple antenna techniques have also been proposed but these are complex in terms of hardware implementation and therefore are suited to base station applications and not mobile applications. Further, all of the above techniques are non-trivial in implementation and/or complexity.
Thus, there is a need for a Single Antenna Interference Cancellation (SAIC) solution for reducing the effect of co-channel interfering signals that is suitable for implementation in mobile handsets, is relatively simple to implement, does not have high MIPS consumption and does not significantly increase cost.